Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks

A system based on the use of artificial neural networks allowing discrimination of strain and temperature in a conventional Brillouin optical time domain analyzer setup is presented and demonstrated in this paper. This solution allows to perform an automatic discrimination of both parameters without...

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Autores: Ruiz Lombera, Rubén|||0000-0002-4604-5787, Fuentes Cayón, Alberto, Rodríguez Cobo, Luis|||0000-0002-2068-2956, López Higuera, José Miguel|||0000-0002-8615-8487, Mirapeix Serrano, Jesús María|||0000-0002-6035-0139
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/15663
Acceso en línea:http://hdl.handle.net/10902/15663
Access Level:acceso abierto
Palabra clave:Artifical neural network
Distributed systems
Optical fiber sensors
Stimulated Brillouin scattering
Strain-temperature discrimination
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repository_id_str
spelling Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networksRuiz Lombera, Rubén|||0000-0002-4604-5787Fuentes Cayón, AlbertoRodríguez Cobo, Luis|||0000-0002-2068-2956López Higuera, José Miguel|||0000-0002-8615-8487Mirapeix Serrano, Jesús María|||0000-0002-6035-0139Artifical neural networkDistributed systemsOptical fiber sensorsStimulated Brillouin scatteringStrain-temperature discriminationA system based on the use of artificial neural networks allowing discrimination of strain and temperature in a conventional Brillouin optical time domain analyzer setup is presented and demonstrated in this paper. This solution allows to perform an automatic discrimination of both parameters without compromising the complexity or cost of the interrogation unit. The classification results, achieved by considering a preprocessing stage with dimensionality reduction via principal component analysis and spatial filtering, improve those obtained in a previous feasibility study.This work was supported in part by the Projects TEC2013-47264-C2-1-R and TEC2016-76021-C2-2-RIEEE-The Optical SocietyUniversidad de Cantabria20182018-06-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/15663Journal of Lightwave Technology, 2018, 36(11), 2114-2121reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/156632026-06-02T12:39:31Z
dc.title.none.fl_str_mv Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
title Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
spellingShingle Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
Ruiz Lombera, Rubén|||0000-0002-4604-5787
Artifical neural network
Distributed systems
Optical fiber sensors
Stimulated Brillouin scattering
Strain-temperature discrimination
title_short Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
title_full Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
title_fullStr Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
title_full_unstemmed Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
title_sort Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks
dc.creator.none.fl_str_mv Ruiz Lombera, Rubén|||0000-0002-4604-5787
Fuentes Cayón, Alberto
Rodríguez Cobo, Luis|||0000-0002-2068-2956
López Higuera, José Miguel|||0000-0002-8615-8487
Mirapeix Serrano, Jesús María|||0000-0002-6035-0139
author Ruiz Lombera, Rubén|||0000-0002-4604-5787
author_facet Ruiz Lombera, Rubén|||0000-0002-4604-5787
Fuentes Cayón, Alberto
Rodríguez Cobo, Luis|||0000-0002-2068-2956
López Higuera, José Miguel|||0000-0002-8615-8487
Mirapeix Serrano, Jesús María|||0000-0002-6035-0139
author_role author
author2 Fuentes Cayón, Alberto
Rodríguez Cobo, Luis|||0000-0002-2068-2956
López Higuera, José Miguel|||0000-0002-8615-8487
Mirapeix Serrano, Jesús María|||0000-0002-6035-0139
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Artifical neural network
Distributed systems
Optical fiber sensors
Stimulated Brillouin scattering
Strain-temperature discrimination
topic Artifical neural network
Distributed systems
Optical fiber sensors
Stimulated Brillouin scattering
Strain-temperature discrimination
description A system based on the use of artificial neural networks allowing discrimination of strain and temperature in a conventional Brillouin optical time domain analyzer setup is presented and demonstrated in this paper. This solution allows to perform an automatic discrimination of both parameters without compromising the complexity or cost of the interrogation unit. The classification results, achieved by considering a preprocessing stage with dimensionality reduction via principal component analysis and spatial filtering, improve those obtained in a previous feasibility study.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-06-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10902/15663
url http://hdl.handle.net/10902/15663
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv IEEE-
The Optical Society
publisher.none.fl_str_mv IEEE-
The Optical Society
dc.source.none.fl_str_mv Journal of Lightwave Technology, 2018, 36(11), 2114-2121
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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